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Innovative Segmentation Strategies for Melanoma Skin Cancer Detection

Munnangi, Anirudh
http://orcid.org/0000-0003-0372-6483
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510916097483278

Abstract Details

2017, MS, University of Cincinnati, Engineering and Applied Science: Electrical Engineering.
The purpose of this project is to research innovative segmentation algorithms that will be the part of skin cancer detection process. As a part of the thesis, two application specific modeled algorithms have been designed to perform the process of segmentation, which is the second step in the overall process of classification of the image into various cancerous categories. A novel attempt to use a clustering based algorithm to address a segmentation task has been attempted and achieved through this research. Images have been considered in the gray scale mode and an attempt has been made to extract maximum results without color information. Both algorithms developed involve training and testing phases. Also, they are inspired by the power of Neural Networks. Once the segmentation is done, various performance metrics have been calculated and reported along with visual aid regarding how well the segmentation occurred. The performance has also been compared with the commonly used methods in image segmentation and the advantages as well as performance factors are well critiqued and documented to provide a holistic view related to the usage of such algorithms in the concerned topic of skin cancer segmentation. Experimental testing has also been done with images having pre-known ground truth information and the resulting segmented portions as well as quality has been shown.
Carla Purdy, Ph.D. (Committee Chair)
Prabir Bhattacharya, Ph.D. (Committee Member)
Yizong Cheng, Ph.D. (Committee Member)
55 p.
Biomedical Research
Skin Cancer; Melanoma; Neural Netoworks; Self Organized Feature Maps; Mixture of Experts; Image Segmentation

Recommended Citations

Citations

  • Munnangi, A. (2017). Innovative Segmentation Strategies for Melanoma Skin Cancer Detection [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510916097483278

    APA Style (7th edition)

  • Munnangi, Anirudh. Innovative Segmentation Strategies for Melanoma Skin Cancer Detection. 2017. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510916097483278.

    MLA Style (8th edition)

  • Munnangi, Anirudh. "Innovative Segmentation Strategies for Melanoma Skin Cancer Detection." Master's thesis, University of Cincinnati, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510916097483278

    Chicago Manual of Style (17th edition)

ucin1510916097483278
343
© 2017, some rights reserved.Creative Commons License Innovative Segmentation Strategies for Melanoma Skin Cancer Detection by Anirudh Munnangi is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.